Identification and functional characterization of pVHL-dependent cell surface proteins in renal cell carcinoma

The identification of cell surface accessible biomarkers enabling diagnosis, disease monitoring, and treatment of renal cell carcinoma (RCC) is as challenging as the biology and progression of RCC is unpredictable. A hallmark of most RCC is the loss-of-function of the von Hippel-Lindau (pVHL) protein by mutation of its gene (VHL). Using the cell surface capturing (CSC) technology, we screened and identified cell surface N-glycoproteins in pVHL-negative and positive 786-O cells. One hundred six cell surface N-glycoproteins were identified. Stable isotope labeling with amino acids in cell culture-based quantification of the CSC screen revealed 23 N-glycoproteins whose abundance seemed to change in a pVHL-dependent manner. Targeted validation experiments using transcriptional profiling of primary RCC samples revealed that nine glycoproteins, including CD10 and AXL, could be directly linked to pVHL-mediated transcriptional regulation. Subsequent human tumor tissue analysis of these cell surface candidate markers showed a correlation between epithelial AXL expression and aggressive tumor phenotype, indicating that pVHL-dependent regulation of glycoproteins may influence the biologic behavior of RCC. Functional characterization of the metalloprotease CD10 in cell invasion assays demonstrated a diminished penetrating behavior of pVHL-negative 786-O cells on treatment with the CD10-specific inhibitor thiorphan. Our proteomic surfaceome screening approach in combination with transcriptional profiling and functional validation suggests pVHL-dependent cell surface glycoproteins as potential diagnostic markers for therapeutic targeting and RCC patient monitoring

The Human Surfaceome: Chemoproteomic Technologies and Bioinformatic Resources for the Analysis of the Surfaceome Interconnectivity

Systems biology aims to quantitatively understand a biological system (i.e., a cell) as a whole. The first cornerstone to achieve a system-wide characterization of the cell was the complete sequencing of the human genome, which encodes the basic information for building the whole protein repertoire of a cell. Next, mass spectrometry-based strategies opened the opportunity to determine the transcribed and translated gene repertoire of the cell - the proteotype. The proteotype bridges the gap inbetween genotype and phenotype and is defined as the actual state of the proteome of a cell. However, to decipher the complexity of biological systems, the definition of the individual components is not sufficient to understand cellular function. One needs to appreciate that a cell is more than the sum of its parts and that biological function is encoded in interaction networks. These protein interaction networks ultimately define protein function and a molecular understanding of such interactions thus enables the analysis of context-dependent cellular signaling. This thesis focuses on proteins exposed to the extracellular space, termed surfaceome, which guide communication of a cell with its outside world. Hence, the surfaceome has a crucial function as gatekeeper, enabling but also limiting cellular communication. Extracellular signals are turned into intracellular signaling responses through the surfaceome in form of ligand receptor interactions. To gain a system-wide understanding of the surfaceome, the identity, quantity and interactions thereof need to be defined. However, most of the available surfaceome information was solely built on the detection of cell surface proteins by a limited pool of antibodies since cell surface proteins were inherently difficult to analyze using other technologies. Only limited information was available about the surfaceome protein repertoire of a cell; a systematic assessement of the variability of the surfaceome over different cell types was absent; only semi-quantitative information about cell surface proteins has been obtained and there was no conception and very sparse molecular knowledge about the interconnectivity between surfaceome proteins. Hence, the aim of this thesis was to identify and quantify cellular surfaceomes, to determine the surfaceome members and to develop technologies for enabling the investigation of the interconnectivity of surfaceome residing proteins. First, the possibilities and the motivation to uncover the biomedical potential of the surfaceome interaction network are discussed in detail within the introductory chapter one, which was written in the form of a review article (chapter 1). i To follow the systematical approach to functionally define a system by first identifying and quantifying its components and then determining its interactions, we set out to first define the identity of the surfaceome (chapter 2). This was necessary, because the available surfaceome maps were limited to the ~300 Cluster of Differentiation (CD) antibody panel. Surfacome sets of 41 different human and 31 different mouse cell types, which were previously collected in a collaborative effort by applying the Cell Surface Capture (CSC, Wollscheid et al, 2009), were used to build the Cell Surface Protein Atlas (CSPA, wlab.ethz.ch/cspa). The combination of these surfaceome datasets revealed nearly 1500 human and 1300 mouse cell surface proteins, which is a five-fold gain compared to the CD antibody panel. Integrated analysis of the CSPA showed that the concerted biological function of individual cell types is mainly guided by quantitative rather than qualitative surfaceome differences. The CSPA is a unique and highly appreciated experimental surfaceome resource demonstrated by 800 monthly website views and an increasing number of citations of the resulted publication. Moreover, the CSPA provided a first blueprint of the interaction space of the surfaceome. To further extend and refine our surfaceome definition, a bioinformatic strategy was developed to create an in silico definition of the surfaceome (chapter 3). Availabe bioinformatics predictions all relied on the same gene annotation databases, which were themselves relying on the limited experimental basis for surfaceome identities, as outlined above. With the CSPA, we had an excellent experimentally validated surfaceome at hand to use as positive training set for a machine learning approach in order to learn characteristic properties of extracellular domains from surfaceome proteins. A model, which incorporated five discriminant biochemical features of extracellular domains of surfaceome proteins was described and then used to predict 2886 potential human surfaceome proteins. On a large cell line panel of 610 cancer cell lines, over 2300 surfaceome genes were found to be expressed in total, with an average of 800 surfaceome genes per cell lines. Interestingly, primary stem cells and their derivatives expressed in average more than twice as many surfaceome proteins. This in silico surfaceome is the first comprehensive and most accurate definition of the surfaceome and is the basis for all future surfaceome interrogations. This resource is available under wlab.ethz.ch/surfaceome, which also provides user-based visualisations of surfaceomes. With the CSPA and the in silico surfaceome, the global and cell type specific identity of the surfaceome was defined, accomplishing the first step towards a system-wide understanding of the surfaceome. ii The next step in the systemic assessment of the surfaceome was to investigate the surfaceome interaction network. Since current protein-protein interaction (PPI) technologies were hardly applicable to cell surface proteins, it was necessary to develop and tailor PPI technologies to specifically target the surfaceome. The concept of radical based biotinylation was applied and combined with hydrazide chemistry to first functionalize and than target glycans at the cell surface. The Proximity Radical Tagging (PRT) technology was established in order to reveal lateral surfaceome interactions of specific receptors (chapter 4). Proof-of-concept applications, like the detection of proteins associated with lipid rafts and heterodimer partners from Erbb2 and the toll-like receptors demonstrated that PRT is very sensitive and able to reveal proximity information of surfaceome members (chapter 4). PRT was then used to investigate the nanoscale organization of the surfaceome in a larger scale on different cell lines (chapter 5). Several cell surface proteins were targeted by PRT and evidence was found that certain lateral surfaceome neighbourhoods change between cell types, whereas other interactions are stable. It was further demonstrated that the strength of the interaction and probably also a distance constraint could be revealed by appropriate experimental setup. The PRT technology opens for the first time the possibility to accomplish the last step of a systemic elucidation of the surfaceome. Further interrogations of the surfaceome interaction network will reveal new mechanistic and functional insight into the surfaceome with crucial implications for the development of novel therapeutics, as for example multitarget drugs. In summary, the first experimental and bioinformatic definition of the surfaceome was achieved, outlining the protein repertoire of the surfaceome. Further, a tailored technology for the investigation of the lateral nanoscale organization and the interconnectivity of the surfaceome was developed. The CSPA and the in silico surfaceome are both provided as tools for the community, for the rediscovery of surfaceome proteins. The PRT technology allows for systematic, large-scale surfaceome interaction screens in order to further elucidate the functional consequences of dynamic nanoscale changes at the cell surface

Detection of protein complex interactions via a blue native-page retardation assay

We describe the Blue Native (BN)-PAGE retardation assay for the detection of interactions of biomolecules with protein complexes. Potential interactors of proteins are included in the BN gel matrix, resulting in retardation of proteins that interact with the added molecule. After validation using the T-cell antigen receptor, we applied the assay for a general identification of dextran interactors in combination with mass spectroscopy. The proteomic screen revealed triosephosphate isomerase oligomer as a dextranbinding, high M_R complex

Proteomic cell surface phenotyping of differentiating acute myeloid leukemia cells

Immunophenotyping by flow cytometry or immunohistochemistry is a clinical standard procedure for diagnosis, classification, and monitoring of hematologic malignancies. Antibody-based cell surface phenotyping is commonly limited to cell surface proteins for which specific antibodies are available and the number of parallel measurements is limited. The resulting limited knowledge about cell surface protein markers hampers early clinical diagnosis and subclassification of hematologic malignancies. Here, we describe the mass spectrometry based phenotyping of 2 all-trans retinoic acid treated acute myeloid leukemia model systems at an unprecedented level to a depth of more than 500 membrane proteins, including 137 bona fide cell surface exposed CD proteins. This extensive view of the leukemia surface proteome was achieved by developing and applying new implementations of the Cell Surface Capturing (CSC) technology. Bioinformatic and hierarchical cluster analysis showed that the applied strategy reliably revealed known differentiation-induced abundance changes of cell surface proteins in HL60 and NB4 cells and it also identified cell surface proteins with very little prior information. The extensive and quantitative analysis of the cell surface protein landscape from a systems biology perspective will be most useful in the clinic for the improved subclassification of hematologic malignancies and the identification of new drug targets

BioID Reveals Novel Proteins of the Plasmodium Parasitophorous Vacuole Membrane

During their development within the vertebrate host, parasites infect hepatocytes and red blood cells. Within these cells, parasites are surrounded by a parasitophorous vacuole membrane (PVM). The PVM plays an essential role for the interaction of parasites with their host cells; however, only a limited number of proteins of this membrane have been identified so far. This is partially because systematic proteomic analysis of the protein content of the PVM has been difficult in the past, due to difficulties encountered in attempts to separate the PVM from other membranes such as the parasite plasma membrane. In this study, we adapted the BioID technique to -cultivated blood stage parasites and utilized the promiscuous biotin ligase BirA* fused to PVM-resident exported protein 1 to biotinylate proteins of the PVM. These we further processed by affinity purification, liquid chromatography-tandem mass spectrometry (LC-MS/MS), and label-free quantitation, leading to a list of 61 known and candidate PVM proteins. Seven proteins were analyzed further during blood and liver stage development. This resulted in the identification of three novel PVM proteins, which were the serine/threonine protein phosphatase UIS2 (PlasmoDB accession no. PBANKA_1328000) and two conserved proteins with unknown functions (PBANKA_0519300 and PBANKA_0509000). In conclusion, our report expands the number of known PVM proteins and experimentally validates BioID as a powerful method to screen for novel constituents of specific cellular compartments in . Intracellular pathogens are often surrounded by a host-cell derived membrane. This membrane is modified by the pathogens to their own needs and is crucial for their intracellular lifestyle. In parasites, this membrane is referred to as the PVM and only a limited number of its proteins are known so far. Here, we applied in rodent parasites a method called BioID, which is based on biotinylation of proximal and interacting proteins by the promiscuous biotin ligase BirA*, and demonstrated its usefulness in identification of novel PVM proteins

BioID Reveals Novel Proteins of the Parasitophorous Vacuole Membrane.

During their development within the vertebrate host, parasites infect hepatocytes and red blood cells. Within these cells, parasites are surrounded by a parasitophorous vacuole membrane (PVM). The PVM plays an essential role for the interaction of parasites with their host cells; however, only a limited number of proteins of this membrane have been identified so far. This is partially because systematic proteomic analysis of the protein content of the PVM has been difficult in the past, due to difficulties encountered in attempts to separate the PVM from other membranes such as the parasite plasma membrane. In this study, we adapted the BioID technique to -cultivated blood stage parasites and utilized the promiscuous biotin ligase BirA* fused to PVM-resident exported protein 1 to biotinylate proteins of the PVM. These we further processed by affinity purification, liquid chromatography-tandem mass spectrometry (LC-MS/MS), and label-free quantitation, leading to a list of 61 known and candidate PVM proteins. Seven proteins were analyzed further during blood and liver stage development. This resulted in the identification of three novel PVM proteins, which were the serine/threonine protein phosphatase UIS2 (PlasmoDB accession no. PBANKA_1328000) and two conserved proteins with unknown functions (PBANKA_0519300 and PBANKA_0509000). In conclusion, our report expands the number of known PVM proteins and experimentally validates BioID as a powerful method to screen for novel constituents of specific cellular compartments in . Intracellular pathogens are often surrounded by a host-cell derived membrane. This membrane is modified by the pathogens to their own needs and is crucial for their intracellular lifestyle. In parasites, this membrane is referred to as the PVM and only a limited number of its proteins are known so far. Here, we applied in rodent parasites a method called BioID, which is based on biotinylation of proximal and interacting proteins by the promiscuous biotin ligase BirA*, and demonstrated its usefulness in identification of novel PVM proteins

The in silico human surfaceome

Cell-surface proteins are of great biomedical importance, as demonstrated by the fact that 66% of approved human drugs listed in the DrugBank database target a cell-surface protein. Despite this biomedical relevance, there has been no comprehensive assessment of the human surfaceome, and only a fraction of the predicted 5,000 human transmembrane proteins have been shown to be located at the plasma membrane. To enable analysis of the human surfaceome, we developed the surfaceome predictor SURFY, based on machine learning. As a training set, we used experimentally verified high-confidence cell-surface proteins from the Cell Surface Protein Atlas (CSPA) and trained a random forest classifier on 131 features per protein and, specifically, per topological domain. SURFY was used to predict a human surfaceome of 2,886 proteins with an accuracy of 93.5%, which shows excellent overlap with known cell-surface protein classes (i.e., receptors). In deposited mRNA data, we found that between 543 and 1,100 surfaceome genes were expressed in cancer cell lines and maximally 1,700 surfaceome genes were expressed in embryonic stem cells and derivative lines. Thus, the surfaceome diversity depends on cell type and appears to be more dynamic than the nonsurface proteome. To make the predicted surfaceome readily accessible to the research community, we provide visualization tools for intuitive interrogation (wlab.ethz.ch/surfaceome). The in silico surfaceome enables the filtering of data generated by multiomics screens and supports the elucidation of the surfaceome nanoscale organization.ISSN:0027-8424ISSN:1091-649

Mass-spectrometric identification and relative quantification of N-linked cell surface glycoproteins.

Although the classification of cell types often relies on the identification of cell surface proteins as differentiation markers, flow cytometry requires suitable antibodies and currently permits detection of only up to a dozen differentiation markers in a single measurement. We use multiplexed mass-spectrometric identification of several hundred N-linked glycosylation sites specifically from cell surface-exposed glycoproteins to phenotype cells without antibodies in an unbiased fashion and without a priori knowledge. We apply our cell surface-capturing (CSC) technology, which covalently labels extracellular glycan moieties on live cells, to the detection and relative quantitative comparison of the cell surface N-glycoproteomes of T and B cells, as well as to monitor changes in the abundance of cell surface N-glycoprotein markers during T-cell activation and the controlled differentiation of embryonic stem cells into the neural lineage. A snapshot view of the cell surface N-glycoproteins will enable detection of panels of N-glycoproteins as potential differentiation markers that are currently not accessible by other means

SV40 at its entry triggers the upregulation of a number of integrins on the cell surface.

<p>(A) Cell surface N-glycoproteins that are significantly altered in cell surface abundance upon exposure to SV40 are visualized with a network view. The glycoproteins whose abundance was either increased (yellow) or decreased (blue) during exposure to SV40 (and which either did not change during exposure to VSV, or changed in the opposite direction compared to SV40) are depicted as nodes in the network. The different shades represent different degrees of relative abandunce (log2 values). The remaining nodes in the network are the hits from the RNAi screen (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055799#pone-0055799-g002" target="_blank">Figure 2A</a>), which either increased (green) or decreased (red) SV40 infection upon siRNA knockdown. For the common hits in the CSC and RNAi screens, the node border represents the RNAi phenotype (ITGA6, ITGB6 and CD47 were CSC-hits but gave no RNAi phenotype when tested). The grey connecting lines between nodes illustrate protein interactions, which were assessed using the STRING database with a combined score of at least 0.9 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055799#pone.0055799-Szklarczyk1" target="_blank">[45]</a>, and were visualized using Cytoscape (<a href="http://www.cytoscape.org" target="_blank">www.cytoscape.org</a>) and the Cerebral plugin <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055799#pone.0055799-Barsky1" target="_blank">[46]</a>.</p

Cell adhesion-signaling components are required for SV40 infection. Integrins, in addition to GM1 lipids, are required for SV40 binding and infection.

<p>(A) A targeted siRNA screen reveals several structural and signaling components of cell adhesion to regulate the SV40 infectious route. A set of four siRNAs against 263 genes was applied in A431 human epithelial cells and virus infection was assessed by the presence of nuclear large T-antigen. Low-resolution imaging and image processing with the CellProfiler analysis software were subsequently performed. A Support Vector Machine (SVM)-based classification method <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055799#pone.0055799-Rm1" target="_blank">[47]</a> was then used to determine percentage of infection upon siRNA treatment. The table shows the genes that reduced (red shades) or enhanced (green shades) SV40 infection with different strength when knocked down. The values in the boxes represent the number of different siRNAs that gave a similar phenotype. (B) Epistasis analysis between Cav1, GRAF1, and Ezrin. A431 cells were treated with siRNA against each one of these genes or combinations of two. Two or three siRNAs were used per gene. Cells were subsequently treated with SV40 and infection levels were assessed by the presence of nuclear T-antigen. The graph shows values pooled from the individual infection indices. p-values: 1.3×10⁻⁴ (Ezrin-siRNA, Cav1-siRNA), 0.39 (Ezrin-siRNA, GRAF1-siRNA). (C) Blocking integrin α2 function with an antibody reduces SV40 infection, similar to siRNA-mediated knock down. A431 cells were pre-incubated with 0.02 µg/µL of blocking antibody 20 min prior to infection (p-values 1×10^−4–7×10⁻⁴). (D) siRNA against integrins α2 and β1 reduces binding of SV40 at the surface of A431 cells. Binding was performed at cold for 2 h and binding capacity was determined by immunoblotting for the presence of the major capsid protein VP1 in cell extracts. Signal intensity was quantified by the ImageJ software and standard deviation corresponds to two independent experiments. (E) SV40 binds onto the surface of various cell lines with different intensity; GM1-deficient cells retain the ability to bind SV40. Quantification of signal intensity from two independent experiments was performed as in (D). (F) SV40-like particles (VLPs) can bind cells that lack its native receptor GM1 in a dose-dependent manner. (G) SV40 can bind cells that lack its native receptor GM1 via integrins. GM95 cells were treated with siRNA against integrin α2 and SV40 binding was determined by the abundance of VP1 protein, as described in (D). (H) Integrins can serve as binding sites for SV40. Integrin α2β1 was immunoprecipitated from A431 cells pretreated for 2 h with SV40 in the cold, and the VP1 protein was detected in the immunocomplex by immunoblotting (red box). Transferrin receptor was used as a negative control.</p